1. Technical Field
The present invention relates generally to a system for eliminating clutter in multi-scan data and more particularly to a system which identifies correlated data between scans by detecting alignment of data after successive shifts in the positions of the scans.
2. Discussion
The processing of multiple sets of two dimensional data to detect correlations between sets is an important computational task in many applications. Where the number of data points is large the processing time can quickly grow so as to preclude real-time processing. One example is the task of correlating multiple scans of two dimensional data gathered by one or more scanning sensors covering a common surveillance region. Currently this problem does not a have practical real-time solution where the number of plots per scan is over one hundred thousand.
In a typical application, it is desired to track nearly constant velocity targets in extremely high uncorrelated clutter fields over as many as eight scans. For example, clutter densities may be in excess of 0.3 objects/mile.sup.2. With more than one hundred thousand clutter plots per scan, it is necessary to reduce the clutter level by three to four orders of magnitude for the remaining clutter objects to be handled by conventional techniques. Conventional technologies would include, for example, alpha-beta, Kalman, and multi-hypothesis trackers. Traditional tracking techniques (such as Kalman techniques) are inadequate and computationally overwhelmed when addressing scenarios of this size, primarily due to the sequential nature of such approaches. Other prior techniques for processing this class of problem include wave front processors. These are processors which perform correlations by emitting waves at separate times from intermediate scan detections, as represented in 2-D cellular automata structures. The intersections of these waves with detection in previous and subsequent scans are characterized by a central controller to determine if the detection belongs to a valid track. This process is bottlenecked by the central controller and the potentially time-consuming nature of sequentially emitting hundreds of thousands of waves.
Thus, it would be desirable to provide a system which can process multiple scans of two dimensional data to detect correlation among large sets of data in a completely parallel manner. Further, it would be desirable to provide such a system which can accomplish this task in real-time. Furthermore it would be desirable to provide such a system in which the total processing time does not increase with the number of data points, or scans.